SOOP: A Swarm-Optimized Opinion Prediction Model for S-Health Governance

Abstract

Background: To realize a viable and resilient smart city-smart nation scenario, “peoplecentric” strategic technology components are imperative to eventually create smart outcomes for citizens. Smart health is one such domain where the government is putting incessant effort to ensure social well-being and sustainability. Contemplation of public opinion plays a very significant role in the process of government policy evaluation. The current affordable, ubiquitous generation of Web provides substantial amount of opinionated social big data which facilitates data-driven decision making. But determining the polarity of short-text, a.k.a. sentiment is hard owing to the noisy, ambiguous and heterogeneous use of natural language. Objective: A novel health governance framework using a Swarm Optimized Opinion Prediction model, SOOP Model is proposed to capture netizen views on government policies and figure out the inclination of public about the campaign. The model is investigated for the sentiment classification task on tweets pertaining to ‘Poshan Abhiyaan’, one of the latest healthcare policy, launched by the Government of India to address the issues related to malnutrition in women and children. Methods: The conventional feature extraction using TF-IDF (Term Frequency-Inverse Document Frequency) is done on the pre-processed dataset. Subsequently, Binary bat algorithm, a swarm-based optimal feature selection method is used to improve the sentiment classifier’s performance. Results: The empirical analysis validates that the proposed implementation of the meta-heuristic algorithm for feature subset selection outperforms the baseline supervised learning algorithms with an average 9.4% improvement in accuracy and approximately 39% average reduction in features. Conclusion: The proposed SOOP Model as a policy evaluation strategy within the healthcare setting empowers various stakeholders and enhances their socio-economic environment.